Identification of Treatment Effects Under Conditional Partial Independence
Copyright policy )Identification of Treatment Effects Under Conditional Partial Independence
Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.
- University of Iowa United States
- Duke University
- DUKE UNIVERSITY
- Departement of Economics United States
- Duke University
FOS: Computer and information sciences, Nonparametric robustness, conditional independence, Econometrics (econ.EM), robustness, Nonparametric inference, treatment effects, partial identification, selection on observables, Methodology (stat.ME), FOS: Economics and business, sensitivity analysis, unconfoundedness, Nonparametric estimation, nonparametric identification, Statistics - Methodology, Economics - Econometrics
FOS: Computer and information sciences, Nonparametric robustness, conditional independence, Econometrics (econ.EM), robustness, Nonparametric inference, treatment effects, partial identification, selection on observables, Methodology (stat.ME), FOS: Economics and business, sensitivity analysis, unconfoundedness, Nonparametric estimation, nonparametric identification, Statistics - Methodology, Economics - Econometrics
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